Accepted Manuscript

Learning Analytics: Survey data for measuring the impact of study satisfaction onstudents’ academic self-efficacy and performance

Dr. Petros Kostagiolas, Dr. Charilaos Lavranos, Dr. Nikolaos Korfiatis

PII: S2352-3409(19)30404-4

DOI: https://doi.org/10.1016/j.dib.2019.104051

Article Number: 104051

Reference: DIB 104051

To appear in: Data in Brief

Received Date: 21 February 2019

Revised Date: 12 May 2019

Accepted Date: 15 May 2019

Please cite this article as: P. Kostagiolas, C. Lavranos, N. Korfiatis, Learning Analytics: Survey data formeasuring the impact of study satisfaction on students’ academic self-efficacy and performance, Data inBrief, https://doi.org/10.1016/j.dib.2019.104051.

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DATA IN BRIEF TEMPLATE

Meta-Data

*Title: Learning Analytics: Survey data for measuring

the impact of study satisfaction on students’

academic self-efficacy and performance

*Authors: Dr. Petros Kostagiolas1

Dr. Charilaos Lavranos1

Dr. Nikolaos Korfiatis2

*Affiliations: 1Department of Archives, Library Science and

Museology, Faculty of Information Science &

Informatics, Ionian University 2Norwich Business School, University of East

Anglia

*Contact email: Dr. Nikolaos Korfiatis n.korfiatis@uea.ac.uk

*Co-authors: Dr. Petros Kostagiolas pkostagiolas@ionio.gr

Dr. Charilaos Lavranos lavranos@ionio.gr

*CATEGORY: Social Sciences: Education

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Data Article

Learning Analytics: Survey data for measuring the impact of study satisfaction

on students’ academic self-efficacy and performance

Authors: Dr. Petros Kostagiolas1; Dr. Charilaos Lavranos

1, Dr. Nikolaos Korfiatis

2;

Affiliations: 1Department of Archives, Library Science and Museology, Faculty of Information

Science & Informatics, Ionian University; 2Norwich Business School, University of East Anglia

Contact email: n.korfiatis@uea.ac.uk

Abstract

This paper presents learning analytics data for measuring the impact of study satisfaction on

students’ academic self-efficacy and performance. For this purpose, a specially designed

questionnaire was developed and distributed across 124 undergraduate students.

Preliminary analysis using descriptive statistics for items and confirmatory factor analysis is

provided. The analysis provides evidence for the relation between students’ satisfaction,

self-efficacy, and academic performance, and evaluates the role of academic information

resources in fulfilling students’ information needs. These data are of importance for

researchers and practitioners involved with budgetary decisions in academic collections as

well as the influence of research specific (rather than training specific) information

resources in student satisfaction.

Keywords: Learning Analytics, study satisfaction, academic self-efficacy, academic

performance, information use, undergraduate students

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Specifications Table

Subject area Social Science

More specific subject

area

Education

Type of data Tables

How data was acquired Hard copy questionnaire

Data format Raw, Analyzed

Experimental factors A qualitative pilot study was performed in the questionnaire

development stage before being distributed to students. The

students asked to self-assess their sense of academic satisfaction,

self-efficacy, and performance. Moreover, they addressed their

information resources usage, as well as the fulfillment of their

information needs.

Experimental features Data was collected using hard copy forms to all students eligible

for participation. The response forms were collected by a

volunteer student and given back to the researcher in a closed

envelope. Consent was given by the school board and no personal

identifiable information was required.

Data source location Greece

Data accessibility Data is included in this article

Related research

article

P. Gkorezis, P. Kostagiolas, D. Niakas, Linking exploration to

academic performance: The role of information seeking and

academic self-efficacy, Library Management. 38 (2017) 404–414.

Value of the Data

• The data provided in this paper reveal the role of study satisfaction on students’

academic self-efficacy and performance.

• The dataset is among the very few available containing primary data dealing with

the issue of the impact of study satisfaction on students’ academic self-efficacy and

performance.

• The dataset can be utilized by other researchers in researching the impact of study

satisfaction on students’ academic self-efficacy and performance. It can provide

significant value to those researchers interested in meta-analytic relations between

student satisfaction and academic performance.

• Researchers and practitioners can reproduce and extend this analysis by repeating

the survey in different contexts, i.e., other countries, universities, specific student

groups, etc.

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1. Data

Learning analytics have become a subject of particular importance, especially considering

the abundance of secondary data encompassing all aspects of student trajectories across an

academic curriculum [1,2]. Students’ academic performance is of particular interest to

higher education institutions internationally, in the view of the support services provided to

complement academic services [3]. Understanding the factors that drive students' academic

performance is becoming an important topic for researchers and education policymakers

with several government initiatives been undertaken recently (e.g., UK government

Teaching Excellence Framework - TEF1). As a result, the vital role of factors such as the

academic environment, study habits, educational skills, and personality traits in optimizing

students' academic performance is emphasized and acknowledged in the literature[4].

Nonetheless, there are scant datasets of primary data available to back up the influence of

study satisfaction to students’ academic self-efficacy and performance.

The complementary importance of primary data in that case relies to the multi-dimensional

nature of student performance and the various metrics and methods available to quantify it.

Therefore, the objective of this dataset is biforld. First it aims to provide raw survey data for

measuring students’ academic satisfaction, self-efficacy, and performance. Second it aims to

provide evidence on the impact of study satisfaction on students’ academic self-efficacy and

performance. A hard copy questionnaire was developed and administered to students who

attended an undergraduate course at a Greek regional university. An outline of basic

insights using descriptive statistics and confirmatory factor analysis is provided in the

sections that follow.

2. Experimental design, materials, and methods

The survey was conducted during the second semester of the academic year 2017-2018 and

included a total number of 124 undergraduate students in Greece. Consent was given by the

school board and the data collection procedure was compliant with the privacy policy of the

University. Hard copy response forms were distributed to all students of the academic

program and no sampling was performed. The distribution was done in classroom before

lecture and the forms were collected by a volunteer student and provided back to the

researcher in a closed envelope. Table 1 depicts the questionnaire elements, measurement

types, and associated variable codes. More specifically, the specially designed questionnaire

includes the following sections:

Section A: Demographics: five (5) variables (sex; age; study line; year of study;

familiarity with English).

1 UK Government – Teaching Excellence Framework (Year 2) Specification. Available at

https://www.gov.uk/government/publications/teaching-excellence-and-student-outcomes-framework-

specification

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Section B: Self-assessment of academic satisfaction, self-efficacy, and performance:

nine (9) items obtained from established scales in the literature [5,6]

Section C: Self-assessment of information resources usage: ten (10) variables (Scientific

Databases; Scientific Journals; Encyclopedias, dictionaries and other

reference works; E-learning system; Electronic Portals and Websites; Social

networking sites; Search Engines; Communication with other University

students; Contact with Professor – Instructor; and the general satisfaction

with the information resources available. This was adapted from [7].

Section D: Self-assessment of information needs fulfillment: one (1) variable (In general,

I am able to fill my information needs in my studies). This can also be

considered an outcome variable.

All items were measured using a 5-point Likert type scale with options at 1 = “not at all”, 2 =

“a little”, 3 = “quite a bit”, 4 = “a lot” and 5 = “very much”. Using Confirmatory Factor

Analysis (CFA) . Items from Sections A to C have been also utilized by other studies in the

literature [8–10].

[Insert Table 1 about here]

Table 2 presents the demographic characteristics of the responded students which comprise

of sex, age, study direction, year of study, as well as familiarity with English. The gender

distribution of the respondents shows that 17.1% are males; the average age is 21.47; while

the study program specialization of the respondents is 43.2% Archives, 12.6% Library

Science, and 44.1% Museology. Furthermore, the study year of the respondents is 35.8%

2nd, 27.5% 3rd, 24.2% 4th, and 11.7% extension, as well as their familiarity with English, is

9.6% a little, 35.7% quite a bit, 38.3% a lot, and 16.5% very much.

[Insert Table 2 about here]

In order to provide a meaningful structure and usefulness to other researchers, especially in

relation with latent factors involved with the design of the questionnaire, we followed all

the procedural remedies for confirmatory factor analysis discussed in [11]. Table 3 provides

the results for item loadings and reliability changes for academic self-efficacy (E1),

satisfaction (E2) and performance (E3).

[Insert Table 3 about here]

To evaluate cases of multicollinearity between factor items and outcome variables, the

item-correlation matrix (Table 4) is provided and show no concerns (maximum inter-item

correlation less than 0.70).

[Insert Table 4 about here]

The factor correlation matrix between the three identified factors (E1, E2, E3) and the two

outcome variables of interest (C10, D1) is provided in Table 5. For the factor structure, the

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square root of the average variance extracted from CFA is reported in the diagonal and is

higher than the reported factor correlation, thus satisfying discriminant validity using the

Fornell-Larcker criterion [12].

[Insert Table 5 about here]

This dataset shows that factors such as study satisfaction combined with a sense of self-

efficacy may act as an essential mechanism for improving students’ academic performance

and the overall satisfaction with their studies. On the other hand, academic information

resources usage assessment is continually improving affecting students’ academic

performance as well, especially in cases of research-oriented instruction. This dataset can

help researchers and institutions (universities, scholars, students, etc.) to comprehend the

important role and the impact of academic program satisfaction to students’ academic self-

efficacy and performance. With the ongoing trend in the deployment of learning analytics,

the outcomes showcase that potential investments in academic information services to

support the educational and research process may result in an improvement in students’

academic performance. The latter is important to administrators and policymakers

considering the important budgetary decisions related to the allocation of funds for

academic subscriptions of teaching and learning vs. research-oriented material. Enrichment

activities of the presented data could also target areas of improvement related to

assessment and other primary sources of academic achievement [13].

Funding Resources

This research did not receive any specific grant from funding agencies in the public,

commercial, or not-for-profit sectors.

Acknowledgments

Authors would like to help participants for comments and suggestions at the 16th

Joint

Information Systems Committee (JISC) learning analytics workshop (London, UK).

References

[1] N. Korfiatis, Big Data for Enhancing Learning Analytics: A Case for Large-Scale

Comparative Assessments, in: Metadata and Semantics Research 7th International

Conference, MSTR 2013, Communications in Computer and Information Science,

Springer-Verlag Berlin Heidelberg, 2013: pp. 225–233.

[2] D.T. Tempelaar, B. Rienties, B. Giesbers, In search for the most informative data for

feedback generation: Learning Analytics in a data-rich context, Computers in Human

Behavior. 47 (2015) 157–167.

[3] P.A. Kostagiolas, N. Korfiatis, M. Poulos, A Long-Tail inspired measure to assess resource

use in information Services, Library & Information Science Research. 34 (2012) 317–323.

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[4] M. Credé, N.R. Kuncel, Study habits, skills, and attitudes: The third pillar supporting

collegiate academic performance, Perspectives on Psychological Science. 3 (2008) 425–

453.

[5] G.M. Spreitzer, Psychological empowerment in the workplace: Dimensions,

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[7] S. Serap Kurbanoglu, B. Akkoyunlu, A. Umay, Developing the information literacy self-

efficacy scale, Journal of Documentation. 62 (2006) 730–743.

[8] P.A. Kostagiolas, F. Samioti, G. Alexias, N. Korfiatis, D. Niakas, Examining Patterns of

Information Behavior Among Healthcare Professionals: A Case Study on Health

Psychologists, New Review of Information Networking. 17 (2012) 108–119.

[9] P.A. Kostagiolas, C. Lavranos, S. Papavlasopoulos, N. Korfiatis, J. Papadatos, Music,

Musicians, and Information Seeking Behavior: A Case Study on a Community Concert

Band, Journal of Documentation. 71 (2015) 3–24.

[10] P.A. Kostagiolas, N. Korfiatis, P. Kourouthanassis, G. Alexias, Work-related factors

influencing doctors search behaviours and trust towards medical information resources,

International Journal of Information Management. 34 (2014) 80–88.

[11] J.B. Schreiber, A. Nora, F.K. Stage, E.A. Barlow, J. King, Reporting structural equation

modeling and confirmatory factor analysis results: A review, The Journal of Educational

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[12] C. Fornell, D.F. Larcker, Evaluating structural equation models with unobservable

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[13] P. Gkorezis, P. Kostagiolas, D. Niakas, Linking exploration to academic performance:

The role of information seeking and academic self-efficacy, Library Management. 38

(2017) 404–414.

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Index of Tables

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Table 1: Questionnaire elements, measurement types and associated variable codes

Code Question Measurement Type

A1 Sex Nominal (Categories: 1=Male, 2=Female)

A2 Age Range

A3 Study Direction Nominal (Categories: 1=Archives, 2=Library

Science, 3=Museology)

A4 Year of Study Ordinal Scale (Categories: 1=1st

Year, 2=2nd

Year,

3=3rd

Year, 4=4th

Year, 5=Extension)

A5 Familiarity with English Ordinal Scale (Categories: 1=Not at all, 2=A little,

3=Quite a bit, 4=A lot, 5=Very much) B1 I am confident about my ability to do my job

B2 I am self-assured about my capabilities to

perform my wok activities

B3 I have mastered the skills necessary for my job

B4 All in all, I am satisfied with my job

B5 In general, I do not like my job

B6 In general, I like working here

B7 I perform tasks that are expected of me

B8 I meet formal performance requirements of the

job

B9 I am involved in activities that are relevant to

my yearly performance assessment

C1 Scientific Databases (e.g., PubMed)

C2 Scientific Journals (e.g., International Journal on

Digital Libraries)

C3 Encyclopedias, dictionaries and other reference

works

C4 E-learning system (e-class)

C5 Electronic Portals and Websites (e.g., Thematic

Portals of the University)

C6 Social networking sites (e.g., Facebook,

LinkedIn)

C7 Search Engines (e.g., Google)

C8 Communication with other University students

C9 Contact Professor - Instructor

C10 In general, I am satisfied with the use of

information resources in my studies

D1 In general, I am able to fulfill my information

needs in my studies

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Table 2: Sample Characteristics

Gender (% of Sample)

Males 17.1

Age

Mean (SD) 21.47 (3.6)

Study Direction (% of Sample)

Archives 43.2

Library Science 12.6

Museology 44.1

Year of Study (% of Sample)

1st

Year 0.8

2nd

Year 35.8

3rd

Year 27.5

4th

Year 24.2

Extension 11.7

Familiarity with English (% of Sample)

A little

Quite a Bit 35.7

A lot 38.3

Very Much 16.5

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Table 3: CFA Loadings and Reliability Changes for Academic Self-Efficacy, Satisfaction and

Performance

Factor Item Std. Loading

(t-Value)

Alpha Alpha

if item removed

AVE

Academic Self-efficacy (E1) B1 0.85 (18.36) 0.80 0.70 0.61

B2 0.86 (18.99) 0.72

B3 0.61 (8.12) 0.85

Academic Satisfaction (E2) B4 0.77 (10.66) 0.81 0.59 0.50

B5 0.70 (9.09) 0.68

Β6 0.64 (7.81) 0.73

Academic Performance (E3) B7 0.63 (8.39) 0.75 0.82 0.60

B8 0.87 (17.69) 0.60

B9 0.81 (14.95) 0.76

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Table 4: Item Correlation Matrix

B1 B2 B3 B4 B5 B6 B7 B8 B9 C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 D1

B1 1

B2 0.745***

1

B3 0.552***

0.500***

1

B4 0.254**

0.314***

0.219* 1

B5 0.198* 0.210

* 0.131 0.588

*** 1

B6 0.217* 0.247

* 0.225

* 0.548

*** 0.498

*** 1

B7 0.393***

0.431***

0.298**

0.249**

0.254**

0.333***

1

B8 0.447***

0.429***

0.396***

0.270**

0.130 0.331***

0.563***

1

B9 0.410***

0.399***

0.356***

0.130 0.089 0.171 0.418***

0.684***

1

C1 0.143 0.257**

0.080 0.152 0.087 0.079 0.261**

0.271**

0.171 1

C2 0.286**

0.294**

0.313***

0.258**

0.152 0.219* 0.380

*** 0.223

* 0.142 0.354

*** 1

C3 0.174 0.252**

0.177 0.148 0.128 0.171 0.255**

0.115 0.099 0.181 0.483***

1

C4 0.026 0.046 -0.044 0.161 0.157 0.078 0.077 -0.086 -0.087 -0.046 0.112 0.228* 1

C5 0.131 0.170 0.116 0.142 0.144 0.067 0.122 -0.030 0.091 0.302**

0.187 0.373***

0.226* 1

C6 -0.027 0.072 0.022 0.264**

0.293**

0.120 0.092 -0.017 0.037 0.085 0.108 0.051 0.298**

0.382***

1

C7 -0.162 -0.087 -0.093 0.124 0.156 0.054 0.082 -0.175 -0.122 0.001 0.125 0.212* 0.616

*** 0.204

* 0.333

*** 1

C8 0.119 0.126 0.216* 0.163 0.187 0.021 0.104 0.020 0.074 -0.028 -0.141 0.071 0.319

*** 0.288

** 0.264

** 0.197

* 1

C9 0.013 0.050 0.105 0.209* 0.221

* 0.108 0.150 -0.009 0.042 0.025 0.149 0.369

*** 0.289

** 0.185 0.210

* 0.270

** 0.354

*** 1

C10 0.229* 0.288

** 0.202

* 0.291

** 0.319

*** 0.198

* 0.346

*** 0.226

* 0.134 0.239

* 0.356

*** 0.476

*** 0.405

*** 0.401

*** 0.358

*** 0.410

*** 0.235

* 0.434

*** 1

D1 0.289**

0.297**

0.230* 0.206

* 0.157 0.027 0.240

* 0.154 0.259

** 0.138 0.296

** 0.128 0.123 0.240

* 0.206

* 0.183 0.088 0.214

* 0.376

*** 1

Computed correlation used pearson-method with listwise-deletion., Note *p<0.05, **p<0.01, ***p<0.001

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Table 5: Factor Correlation Matrix with outcome variables (C10, D1)

E1 E2 E3 C10 D1

E1 0.78

E2 0.449 0.70

E3 0.629 0.291 0.77

C10 0.357 0.393 0.269 -

D1 0.344 0.377 0.274 0.413 -

Note: Square Root of AVE for the diagonals of Factor Structure (E1-E3)

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Conflict of Interest Declaration

All authors declare that no conflict of interest exists with the attached data and manuscript.

Dr Petros Kostagiolas, Dr Charilaos Lavranos, Dr Nikolaos Korfiatis